{"cells":[{"cell_type":"markdown","metadata":{"id":"Tg0WAGktMOVH"},"source":["# **1: Network Testing**\n","\n","This tutorial demonstrates how to test the double LSTM-based model for the sequence-to-scalar future hysteresis step prediction. The testset seqeunces include data that has the same sampling time steps as the training dataset. The testing data is a single frequency 200kHz data sequence at 25C. Sequence length = 8015 data points\n","\n","\n","# **Step 0: Import Packages**\n","\n","In this step we import the important packages that are necessary for the testing."]},{"cell_type":"code","execution_count":1,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"collapsed":true,"id":"oE_3CSIzfC1d","outputId":"400c72d8-4aa1-41b2-f57e-1c650a8be50e","executionInfo":{"status":"ok","timestamp":1747398230339,"user_tz":240,"elapsed":22524,"user":{"displayName":"Princeton Power Electronics","userId":"04815773103473656676"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["Mounted at /content/drive\n"]}],"source":["from google.colab import drive\n","drive.mount('/content/drive')\n","\n","import torch\n","from torch import Tensor\n","import torch.nn as nn\n","import torch.nn.functional as F\n","import torch.optim as optim\n","import random\n","import numpy as np\n","import json\n","import math\n","import csv\n","import time\n","import h5py\n","import matplotlib.pyplot as plt\n"]},{"cell_type":"markdown","metadata":{"id":"ENIjgL-si0I2"},"source":["# **Step 1: Define Network Structure**\n","The structure of the duel LSTM-based encoder-projector-decoder neural network are defined here. The network structure does not change from the training structure. Refer to the PyTorch document for more details."]},{"cell_type":"code","execution_count":2,"metadata":{"id":"dYMQPMknLUPN","executionInfo":{"status":"ok","timestamp":1747398233029,"user_tz":240,"elapsed":13,"user":{"displayName":"Princeton Power Electronics","userId":"04815773103473656676"}}},"outputs":[],"source":["# Define model structures and functions\n","\n","class Net(nn.Module):\n","    def __init__(self):\n","        super(Net, self).__init__()\n","\n","        self.lstm_B = nn.LSTM(1, 12, num_layers=1, batch_first=True, bidirectional=False)\n","\n","        self.lstm_H = nn.LSTM(1, 12, num_layers=1, batch_first=True, bidirectional=False)\n","\n","        self.projector = nn.Sequential(\n","            nn.Linear(12 *2 + 2 , 12 *2 + 2),\n","            nn.ReLU(),\n","            nn.Linear(12 *2 + 2, 8),\n","            nn.ReLU(),\n","            nn.Linear(8 , 4),\n","            nn.ReLU(),\n","            nn.Linear(4, 1)\n","        )\n","\n","    def forward(self, seq_B: Tensor, seq_H: Tensor, scal: Tensor, T: Tensor, device) -> Tensor:\n","\n","        seq_B = seq_B.float()\n","        seq_H = seq_H.float()\n","        scal = scal.float()\n","        T = T.float()\n","\n","        x_B, _ = self.lstm_B(seq_B)\n","        x_B = x_B[:, -1, :]\n","\n","        x_H, _ = self.lstm_H(seq_H)\n","        x_H = x_H[:, -1, :]\n","\n","\n","        output = self.projector(torch.cat((scal, T, x_B, x_H), dim=1))\n","        output = output.to(device)\n","\n","        return output"]},{"cell_type":"markdown","metadata":{"id":"2CXI2TIOpNJl"},"source":["# **Step 2: Load the Testing Dataset**\n","\n","Dataset needs to be processed before testing. We load the dataset provided by the user, in this case a single seqeunce with a predefined sequences memory length matching the training memory sequence. Output H sequence is post processed again to provide autoregressive function."]},{"cell_type":"code","execution_count":3,"metadata":{"id":"4O__WD8JLUjt","executionInfo":{"status":"ok","timestamp":1747398237231,"user_tz":240,"elapsed":49,"user":{"displayName":"Princeton Power Electronics","userId":"04815773103473656676"}}},"outputs":[],"source":["#Defind parameters\n","def count_parameters(model):\n","    return sum(p.numel() for p in model.parameters() if p.requires_grad)\n","\n","# Load the dataset\n","\n","def get_dataset(data_length):\n","    # Load .json Files\n","    with h5py.File('/content/drive/MyDrive/Colab Notebooks/Minjie Chen/MagNetX/3C90_Testing.h5', 'r') as file:\n","        print(\"keys:\", list(file.keys()))\n","\n","        B_list = []\n","        H_list = []\n","        B_scal_list = []\n","        H_out_list = []\n","        T_scal_list = []\n","\n","        for i in range(4,5):  # i from 1 to 8\n","            B_list.append(file[f'B_seq_f_{i}'][:])\n","            H_list.append(file[f'H_seq_f_{i}'][:])\n","            B_scal_list.append(file[f'B_scal_{i}'][:])\n","            H_out_list.append(file[f'H_scal_{i}'][:])\n","            T_scal_list.append(file[f'T_{i}'][:])\n","            #print(np.size(B_list))\n","\n","        # Now concatenate them after the loop\n","        B = np.concatenate(B_list, axis=0)\n","        H = np.concatenate(H_list, axis=0)\n","        B_scal = np.concatenate(B_scal_list, axis=0)\n","        H_out = np.concatenate(H_out_list, axis=0)\n","        T_scal = np.concatenate(T_scal_list, axis=0)\n","\n","    print(\"Data Loading Initiated\")\n","\n","\n","    # Load from JSON\n","    with open('/content/drive/MyDrive/Colab Notebooks/Minjie Chen/MagNetX/Normalization_Params.json', 'r') as f:\n","        Param = json.load(f)\n","\n","    print(\"Normalization Initiated\")\n","\n","    mean_B = np.array(Param['mean_B'])\n","    std_B = np.array(Param['std_B'])\n","    mean_H = np.array(Param['mean_H'])\n","    std_H = np.array(Param['std_H'])\n","    mean_out = np.array(Param['mean_out'])\n","    std_out = np.array(Param['std_out'])\n","    mean_Scal = np.array(Param['mean_Scal'])\n","    std_Scal = np.array(Param['std_Scal'])\n","    mean_T = np.array(Param['mean_T'])\n","    std_T = np.array(Param['std_T'])\n","\n","    B = np.array(B)\n","    H = np.array(H)\n","    B_scal = np.array(B_scal)\n","    H_out = np.array(H_out)\n","    T_scal = np.array(T_scal)\n","\n","    B_scal = B_scal.reshape(-1, 1)\n","    T_scal = T_scal.reshape(-1, 1)\n","    in_B = B.reshape(-1,data_length, 1)\n","    in_H = H.reshape(-1,data_length, 1)\n","    out = H_out.reshape(-1,1)\n","\n","    B_plot = B_scal  # For plotting later\n","\n","    normH = [mean_out,std_out ]\n","\n","\n","\n","    T_scal = (T_scal-mean_T)/std_T\n","    B_scal = (B_scal-mean_Scal)/std_Scal\n","    in_B = (in_B-mean_B)/std_B\n","    in_H = (in_H-mean_H)/std_H\n","    H_out = (H_out-mean_out)/std_out\n","\n","    in_H[Nseq : , :] = 0     # Replacing everything after N rows with 0, only retain the first H memory\n","\n","    B_scal = torch.from_numpy(B_scal)\n","    T_scal = torch.from_numpy(T_scal)\n","    in_B = torch.from_numpy(in_B).float().view(-1,data_length, 1)\n","    in_H = torch.from_numpy(in_H).float().view(-1,data_length, 1)\n","    out = torch.from_numpy(H_out).float().view(-1,1)\n","\n","\n","    in_B = in_B.to(dtype=torch.float)\n","    in_H = in_H.to(dtype=torch.float)\n","    B_scal = B_scal.to(dtype=torch.float)\n","    T_scal = T_scal.to(dtype=torch.float)\n","    out = out.to(dtype=torch.float)\n","\n","    print(f\"in_B is {in_B.size()}\")\n","    print(f\"in_H is {in_H.size()}\")\n","    print(f\"B_scal is {B_scal.size()}\")\n","    print(f\"out is {out.size()}\")\n","\n","    return torch.utils.data.TensorDataset(in_B, in_H , B_scal, T_scal, out), normH, B_plot"]},{"cell_type":"markdown","metadata":{"id":"DJcX3ZPesurO"},"source":["# **Step 3: Testing the Model**\n","\n","The loaded dataset is directly used as the test set. The model state dictionary file (.sd) containing all the trained parameter values is loaded and tested."]},{"cell_type":"code","execution_count":4,"metadata":{"vscode":{"languageId":"julia"},"colab":{"base_uri":"https://localhost:8080/","height":731},"id":"kSuz2iM89kEM","outputId":"69e764a7-f97a-41dc-a446-f0dfd4d761f1","executionInfo":{"status":"ok","timestamp":1747398272387,"user_tz":240,"elapsed":26005,"user":{"displayName":"Princeton Power Electronics","userId":"04815773103473656676"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["keys: ['B_scal_4', 'B_seq_f_4', 'H_scal_4', 'H_seq_f_4', 'T_4']\n","Data Loading Initiated\n","Normalization Initiated\n","in_B is torch.Size([8015, 80, 1])\n","in_H is torch.Size([8015, 80, 1])\n","B_scal is torch.Size([8015, 1])\n","out is torch.Size([8015, 1])\n","Number of parameters:  2399\n","Model is loaded!\n","keys: ['B_scal_4', 'B_seq_f_4', 'H_scal_4', 'H_seq_f_4', 'T_4']\n","Data Loading Initiated\n","Normalization Initiated\n","in_B is torch.Size([8015, 80, 1])\n","in_H is torch.Size([8015, 80, 1])\n","B_scal is torch.Size([8015, 1])\n","out is torch.Size([8015, 1])\n","Number of parameters:  2399\n","Model is loaded!\n"]},{"output_type":"display_data","data":{"text/plain":["<Figure size 1200x400 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}],"source":["# Config the model Testing\n","#Defind parameters\n","step_size = 1\n","data_length = 80\n","Nseq = 1  # Number of seqeunces to test. This is also denoted as N no. of seqeunces.\n","\n","def main(state_dict, row):\n","\n","    # Reproducibility\n","    random.seed(1)\n","    np.random.seed(1)\n","    torch.manual_seed(1)\n","    torch.backends.cudnn.deterministic = True\n","    torch.backends.cudnn.benchmark = False\n","\n","    # Hyperparameters\n","    BATCH_SIZE = Nseq # Set the batch size to Nseq becuase we want to retain only the first H memory segment of Nseq number of sequences\n","\n","    # Select GPU as default device\n","    device = torch.device(\"cuda\")\n","\n","    # Load dataset\n","    dataset, normH, B_plot = get_dataset(data_length)\n","\n","    # Split the dataset\n","    kwargs = {'num_workers': 0, 'pin_memory': True, 'pin_memory_device': \"cuda\"}\n","    test_loader =  torch.utils.data.DataLoader(dataset, batch_size=BATCH_SIZE, shuffle=False, **kwargs)\n","    testData = list(test_loader)\n","\n","    # Setup network\n","    net = Net().to(device)\n","\n","    # Log the number of parameters\n","    print(\"Number of parameters: \", count_parameters(net))\n","\n","    # Load trained parameters\n","    net.load_state_dict(state_dict, strict=True)\n","    net.eval()\n","    print(\"Model is loaded!\")\n","\n","    #Test\n","    net.eval()\n","    out_pred = [];\n","    out_pred = torch.tensor(out_pred)\n","    out_meas = [];\n","    out_meas = torch.tensor(out_meas)\n","    outputs = torch.empty((0,1))\n","    outputs = outputs.to(device)\n","\n","\n","    previous_in_H= None\n","    with torch.no_grad():\n","        for in_B, in_H, B_scal, T_scal, out in testData:  # Batch level\n","            # The starting of the first iteration where the first point is predicted using all the H measurement data\n","            if  previous_in_H is None:\n","                outputs = net(seq_B = in_B.to(device), seq_H = in_H.to(device), scal = B_scal.to(device), T = T_scal.to(device), device = device)\n","                out_pred = out_pred.to(outputs.device)\n","                out_pred = torch.cat((out_pred, outputs), dim = 1)\n","                out_meas = out_meas.to(out.device)\n","                out_meas = torch.cat((out_meas, out), dim = 1)\n","            # In the subsequent iterations, in_H is circled through with the predicted H value of the previous step\n","            else:\n","                in_H = np.roll(previous_in_H.cpu(),-1,axis = 1) # shift the values in in_H leftward\n","                in_H[:, -1] = out_pred[:, -1].cpu().numpy().reshape(-1, 1)  # Set the new value at the end with the predicted value\n","                in_H = torch.from_numpy(in_H).float()\n","                outputs = net(seq_B = in_B.to(device), seq_H = in_H.to(device), scal = B_scal.to(device), T = T_scal.to(device), device = device)\n","                out_pred = out_pred.to(outputs.device)\n","                out_pred = torch.cat((out_pred, outputs), dim = 1)\n","                out_meas = out_meas.to(out.device)\n","                out_meas = torch.cat((out_meas, out), dim = 1)\n","\n","            previous_in_H = in_H\n","\n","        # Obtain the tested data\n","        y_meas = out_meas.cpu().numpy()\n","        y_meas = y_meas *normH[1]+normH[0]\n","        y_pred = out_pred.cpu().numpy()\n","        y_pred = y_pred *normH[1]+normH[0]\n","\n","        #### For ploting the test result ####\n","\n","        # Find the ideal linear H sequence using initial permeability u_i\n","        mu = 4 * np.pi * 1e-7 * 2300    #2300 for 3c90\n","        H_fixed = []\n","        B_plot = B_plot.reshape(BATCH_SIZE, -1)\n","        H_curr = y_meas[row, 0]\n","\n","        # Loop over columns, except the last one\n","        for j in range(y_meas.shape[1] - 1):\n","            delta_B = B_plot[row, j+1] - B_plot[row , j]\n","            H_next = H_curr + delta_B / mu\n","\n","            H_fixed.append(H_curr)\n","            H_curr = H_next\n","\n","        H_fixed = np.array(H_fixed).reshape(BATCH_SIZE,-1)\n","\n","    return y_pred, H_fixed, y_meas\n","\n","if __name__ == \"__main__\":\n","\n","    #### For ploting the test result ####\n","    row = 0\n","    pred1,H_fixed1, y_meas1 = main(torch.load('/content/drive/MyDrive/Colab Notebooks/Minjie Chen/MagNetX/Model_LSTM_Princeton.sd'), row)\n","    pred2,H_fixed2, y_meas2 = main(torch.load('/content/drive/MyDrive/Colab Notebooks/Minjie Chen/MagNetX/Model_LSTM_New.sd'), row)\n","    plt.figure(figsize=(12, 4))\n","    plt.plot(y_meas1[row], label=\"Measurement\")\n","    plt.plot(pred1[row], label=\"Prediction_Princeton\")\n","    plt.plot(pred2[row], label=\"Prediction_New\")\n","    plt.plot(H_fixed1[row], label=\"Linear\")\n","    plt.title(f\"Autoregressive H Prediction\")\n","    plt.xlabel(\"Data Points\")\n","    plt.ylabel(\"H (A/m)\")\n","    plt.grid(True)\n","    plt.legend()\n","    plt.show()\n"]}],"metadata":{"accelerator":"GPU","colab":{"gpuType":"T4","machine_shape":"hm","provenance":[]},"kernelspec":{"display_name":"Python 3","name":"python3"},"language_info":{"name":"python"}},"nbformat":4,"nbformat_minor":0}